{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c3a5e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.12.1\n",
      "lightning: 2022.9.15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "292759a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef478510-97a2-497c-8831-bb594b753d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcee9b9-40cf-4083-b36e-b09fe28e4ee2",
   "metadata": {},
   "source": [
    "### With Restarts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9bf7772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs/10)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.plot(lrs)\n",
    "plt.savefig('3-cosine-restarts_lr.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce808943-a20e-44d2-9303-c897226b37df",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PyTorchMLP(torch.nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.all_layers = torch.nn.Sequential(\n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_features, 100),\n",
    "            torch.nn.BatchNorm1d(100),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(100, 50),\n",
    "            torch.nn.BatchNorm1d(50),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # output layer\n",
    "            torch.nn.Linear(50, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.all_layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59a6a00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate, cosine_t_max):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.cosine_t_max = cosine_t_max\n",
    "        self.model = model\n",
    "\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.val_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "\n",
    "        loss = F.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"train_loss\", loss)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"train_acc\", self.train_acc, prog_bar=True, on_epoch=True, on_step=False\n",
    "        )\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"val_loss\", loss, prog_bar=True)\n",
    "        self.val_acc(predicted_labels, true_labels)\n",
    "        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        opt = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n",
    "        sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=self.cosine_t_max)\n",
    "\n",
    "\n",
    "        return {\n",
    "            \"optimizer\": opt,\n",
    "            \"lr_scheduler\": {\n",
    "                \"scheduler\": sch,\n",
    "                \"monitor\": \"train_loss\",\n",
    "                \"interval\": \"epoch\", # step means \"batch\" here, default: epoch\n",
    "                \"frequency\": 1, # default\n",
    "            },\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cf71ad7-04a8-4789-9db5-8051f1472f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3e70623",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffbd91e76a724228ab2e09956f54e48f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n",
      "Learning rate set to 0.15848931924611143\n",
      "Restoring states from the checkpoint path at /Users/sebastianraschka/Desktop/cosine/.lr_find_0be789ae-7eed-42e9-b3ed-6d133ce45ab1.ckpt\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "L.seed_everything(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1, cosine_t_max=num_epochs/10)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=num_epochs,\n",
    "    auto_lr_find=True,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    "    callbacks=callbacks\n",
    ")\n",
    "\n",
    "results = trainer.tune(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0542570a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cb/1y0v6fzx5c54sztp5nqq39jc0000gn/T/ipykernel_36458/735961897.py:3: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = results[\"lr_find\"].plot(suggest=True)\n",
    "# fig.savefig(\"lr_suggest.pdf\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9293189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15848931924611143\n"
     ]
    }
   ],
   "source": [
    "# get suggestion\n",
    "new_lr = results[\"lr_find\"].suggestion()\n",
    "print(new_lr)\n",
    "\n",
    "# update hparams of the model\n",
    "lightning_model.hparams.learning_rate = new_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb1581b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | model     | PyTorchMLP | 15.6 K\n",
      "1 | train_acc | Accuracy   | 0     \n",
      "2 | val_acc   | Accuracy   | 0     \n",
      "3 | test_acc  | Accuracy   | 0     \n",
      "-----------------------------------------\n",
      "15.6 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.6 K    Total params\n",
      "0.062     Total estimated model params size (MB)\n"
     ]
    },
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       "model_id": "",
       "version_major": 2,
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      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
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       "model_id": "2174e4492b514d5c84d9414daf065508",
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      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4de0cfe50644d78a6db21d6f46ac763",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c07124a53f24ee6b64d4d79f511a93a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bb86587d10745c687a929401e9012bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=200` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c6fa764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.ylim([0.7, 1.0])\n",
    "\n",
    "plt.savefig('3-cosine-restarts_acc.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0df61ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_15/checkpoints/epoch=179-step=81000.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_15/checkpoints/epoch=179-step=81000.ckpt\n",
      "/Users/sebastianraschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:231: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10329d430cb542629d614c011194c059",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8932499885559082     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8932499885559082    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8932499885559082}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c17a3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_15/checkpoints/last.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_15/checkpoints/last.ckpt\n"
     ]
    },
    {
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       "model_id": "c1ee7dbc7ac043bba085576822c9a998",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8855000138282776     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8855000138282776    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8855000138282776}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"last\")"
   ]
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